Adaptive control in the presence of input constraints

ABSTRACT

An adaptive control method is provided that scales both gain and commands to avoid input saturation. The input saturation occurs when a commanded input u c  exceeds an achievable command limit of u max . To avoid input saturation, the commanded input u c  is modified according to a factor μ.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No. 60/592,436, filed Jul. 30, 2004, which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

This invention relates generally to model reference adaptive control (MRAC) and more particularly to a direct adaptive control technology that adaptively changes both control gains and reference commands.

BACKGROUND

An adaptive model reference flight control loop is shown in FIG. 1. The flight control loop responds to a scalar reference input r provided by an external guidance system (not illustrated) or from guidance commands from a pilot. For example, an external guidance system may command a climb of a certain number of feet per second and scale input r accordingly. Aircraft 10 includes a set of actuators 15 that operate control surfaces on the aircraft in response to reference input r. In response to the actuation of the control surfaces, aircraft 10 will possess a certain state as measured by sensors 20, denoted by a state vector x. State vector x would include, for example, pitch, roll, and other standard sensor measurements. Similar measurements are provided by a reference model 50 for aircraft 10. Reference model 50 receives reference input r and generates a reference state x_(ref) for aircraft 10 that would be expected in response to the flight control loop receiving reference input r.

An adaptive control system receives an error signal representing the difference between state vector x and reference state x_(ref) and provides a linear feedback/feedforward input command u_(lin). It may be shown that u_(lin) is a function of the sum of the product of a state gain k_(x) and the state vector x and the product of a reference gain k_(r) and the scalar reference input r. Responsive to the measurement of vector x and a scalar reference input r, a baseline or nominal controller 30 generates a nominal control signal 35, u_(lin-nominal). Unlike the adaptive gains used to form u_(lin), u_(lin-nominal) is the sum of the product of a static gain k_(x0) and the state vector x and the product of a static gain k_(r0) and the scalar reference input r. In that regard, a control loop topology could be constructed as entirely adaptive without any nominal control component such as baseline controller 30. However, the nominal control component will assist to “point in the right direction” such that the adaptive control may more quickly converge to a stable solution.

An input command u_(c) is formed from the summation of u_(lin) and u_(lin-nominal). Responsive to the input command u_(c), the appropriate control allocations amongst the various control surfaces are made in control allocation act 40 to provide commands to actuators 15. In turn, actuators 15 implement actual input command u. Under normal conditions, u and u_(c) should be very similar or identical. However, there are limits to what control surfaces can achieve. For example, an elevator or rudder may only be deflectable to a certain limit. These limits for the various control surfaces may be denoted by an input command saturation limit, u_(max). Thus, u can not exceed u_(max) or be less than −u_(max). If u_(c) exceeds u_(max), u will be saturated at limit u_(max).

Conventional linear control such as that shown in FIG. 1 is typically fairly robust to small modeling errors. However, linear control techniques are not intended to accommodate significant unanticipated errors such as those that would occur in the event of control failure and/or a change in the system configuration resulting from battle damage. A common characteristic of conventional adaptive control algorithms is that physical limitations are encountered such as actuator displacement and rate limits such that u becomes saturated at u_(max) or −u_(max). This input saturation may lead to instability such that the aircraft crashes.

Although described with respect to a flight control system, many other types of adaptive control systems share this problem of input saturation. Accordingly, there is a need in the art for improved adaptive control techniques that explicitly accounts for and has the capability of completely avoiding input saturation.

SUMMARY

In accordance with an aspect of the invention, an adaptive control technique is provided in the presence of input constraints. For example, in an aircraft having actuators controlling control surfaces, the actuators may possess an input command saturation of u_(max). Despite these limits, if the aircraft uses an adaptive or nominal control system, the control system may provide a linear feedback/feedforward commanded input of u_(lin) that may exceed u_(max) such that the actual command input realized by the actuators is sataturated at u_(max). The following acts avoid such input saturation: defining a positive input command limit u_(max) ^(δ) equaling (u_(max)−δ), where 0<δ<u_(max); defining a negative input command limit equaling −u_(max) ^(δ); if the absolute value of u_(lin) is less than or equal to u_(max) ^(δ), commanding the actuators with u_(lin); if u_(lin) exceeds u_(max) ^(δ), commanding the actuators with a first command input that is a function of the sum of u_(lin) and a scaled version of u_(max) ^(δ); and if u_(lin) is less than −u_(max) ^(δ); commanding the actuators with a second command input that is a function of the difference of u_(lin) and a scaled version of u_(max) ^(δ).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a conventional model reference flight control loop.

FIG. 2 is a block diagram of an adaptive flight control loop having a baseline controller in accordance with an embodiment of the invention.

FIG. 3 is a block diagram of an entirely adaptive flight control loop in accordance with an embodiment of the invention.

FIG. 4 is a plot of achieved input u as a function of time for a prior art adaptive control loop and an adaptive control loop having a non-zero μ factor in accordance with an embodiment of the invention.

FIGS. 5 a through 5 d demonstrate tracking performance and input commands for various values of μ.

Embodiments of the present invention and their advantages are best understood by referring to the detailed description that follows. It should be appreciated that like reference numerals are used to identify like elements illustrated in one or more of the figures.

DETAILED DESCRIPTION

The present invention provides an adaptive control methodology that is stable in the sense of Lyapunov (theoretically proven stability), yet explicitly accounts for control constraints to completely avoid input saturation. This adaptive control methodology may be better understood with reference to the conventional flight control of FIG. 1. This control loop is described with respect to an aircraft 10. However, it will be appreciated that the adaptive control described herein has wide applications to any adaptive control loop implemented in a system that has input saturation. As discussed with respect to FIG. 1, aircraft 10 includes actuators 15 that respond to commanded inputs u_(c) with an actual or achieved input u as discussed previously. In response to actual input u, aircraft 10 achieves a state x as measured by sensors 20. Given the state x and actual input commands u, an equation for model system dynamics is as follows: {dot over (x)}(t)=Ax(t)+bλu(t), x∈R ^(n) ,u∈R where A is an unknown matrix, b is a known control direction, λ is an unknown positive constant, R is any real number, and R^(n) is an n-dimensional vector.

Should there be no saturation of control surfaces, actual or achieved input commands u and the commanded input u_(c) are identical. However, a typical control surface can only achieve a certain amount of deflection. For example, a rudder or elevator may only be deflectable through a certain angle or limit, which may be denoted as u_(max). Thus, should u_(c) exceed this limit, the actual input u will equal u_(max). This relationship between u_(c) and u may be represented mathematically as:

${u(t)} = {{u_{\max}\mspace{14mu}{{sat}\left( \frac{u_{c}}{u_{\max}} \right)}} = \left\{ \begin{matrix} {{u_{c}(t)},{{{u_{c}(t)}} \leq u_{\max}}} \\ {{u_{\max}\mspace{14mu}{{sgn}\left( {u_{c}(t)} \right)}},{{{u_{c}(t)}} \geq u_{\max}}} \end{matrix} \right.}$ where u_(max) is the saturation level. Based upon this relationship, the equation for the system dynamics may be rewritten as {dot over (x)}=Ax+bλ(u _(c) +Δu), Δu=u−u _(c) Even if u_(c) is limited to u_(max) to avoid input saturation, it will be appreciated that u may approach u_(max) too quickly such that undesired vibrations are incurred as u equals u_(max). Accordingly, a new limit on actual command inputs is introduced as follows u _(max) ^(δ) =u _(max)−δ, where: 0<δ<u _(max) A commanded control deficiency Δu_(c) between the commanded input u_(c) and the actual input may then be represented as

${\Delta\; u_{c}} = {{u_{{ma}x}^{\delta}\mspace{14mu}{{sat}\left( \frac{u_{c}}{u_{\max}^{\delta}} \right)}} - u_{c}}$

The present invention introduces a factor μ into the commanded input u_(c) as follows:

$u_{c} = {\underset{\underset{u_{lin}}{︸}}{{k_{x}^{T}x} + {k_{r}r}} + {\mu\;\Delta\; u_{c}}}$ where k_(x) and k_(r) are the gains for the actual state x and the reference state r, respectively. As discussed with respect to FIG. 1, u_(lin) may be entirely adaptive or possess a nominal component.

Note that u_(c) is implicitly determined by the preceding two equations. It may be solved for explicitly as:

$\begin{matrix} {u_{c} = {{\frac{1}{1 + \mu}\left( {u_{lin} + {\mu\mspace{14mu} u_{\max}^{\delta}\mspace{14mu}{{sat}\left( \frac{u_{lin}}{u_{\max}^{\delta}} \right)}}} \right)} = \left\{ \begin{matrix} {u_{lin},{{u_{lin}} \leq u_{\max}^{\delta}}} \\ {{\frac{1}{1 + \mu}\left( {u_{lin} + {\mu\mspace{14mu} u_{\max}^{\delta}}} \right)},{u_{lin} > u_{\max}^{\delta}}} \\ {{\frac{1}{1 + \mu}\left( {u_{lin} - {\mu\mspace{14mu} u_{\max}^{\delta}}} \right)},{u_{lin} < {- u_{\max}^{\delta}}}} \end{matrix} \right.}} & {{Eq}.\mspace{14mu}(1)} \end{matrix}$ It follows that u_(c) is continuous in time but not continuously differentiable.

To assure Lyapunov stability, it is sufficient to choose the factor μ as follows:

$\mu > {\frac{\left( {\kappa + {2\;\lambda{{Pb}}\left( {{\Delta\; k_{x}^{\max}} + {k_{x}^{*}}} \right)}} \right)u_{\max}}{\kappa\delta} + \frac{\left( {{\Delta\; k_{r}^{\max}} + {k_{r}^{*}}} \right)\kappa\; r_{\max}}{\kappa\delta} - 2}$ where Δk_(x) ^(max), Δk_(r) ^(max) are the maximum initial parameter errors, k_(x)*, k_(r)* are parameters that define the ideal control law for achieving the desired reference model for the given unknown system, and κ is a constant that depends upon the unknown system parameters,

Implementation of the factor μ within an adaptive flight control loop is shown in FIG. 2. A module 90 receives u_(c) that is formed from the addition of u_(lin), and u_(lin-nominal) as discussed with respect to FIG. 1. The factors u_(max), μ and δ discussed with respect to Equation (1) may be provided by an external system or stored within memory (not illustrated). Given these factors, module 90 examines u_(c) and implements Equation (1) accordingly to provide a modified commanded input u_(c)′. From factors u_(max) and δ, module 90 calculates u_(max) ^(δ) so that u_(c) may be compared to u_(max) ^(δ). If the absolute value of u_(c) (or equivalently, u_(lin)) is less than or equal to u_(max) ^(δ), then module 90 provides u_(c) as being equal to u_(c). If, however, u_(c) exceeds u_(max) ^(δ) or is less than −u_(max) ^(δ), module 90 provides u_(c)′ as being equal to the corresponding value from Equation (1). It will be appreciated that module 90 may be implemented within hardware, software, or a combination of hardware and software. Moreover, as seen in FIG. 3, module 90 may be implemented within an entirely adaptive control loop that does not possess a baseline controller 30.

A graphical illustration of the effect of module 90 with respect to the achieved command u and the saturation limits u_(max) and −u_(max) is illustrated in FIG. 4. Consider the case if the factor μ equals zero. Examination of Equation (1) and FIGS. 2 and 3 shows that for such a value of μ, the modified input command u_(c)′ will simply equal u_(c). As discussed in the background section, the achieved input command u will thus saturate at u_(max) as u_(c) exceeds u_(max). Such an input saturation may cause dangerous instability, leading to crashes or other undesirable effects. Conversely, if μ equals the minimum value required for Lyapunov stability as discussed above, the achieved input command u does not saturate, thereby eliminating input saturation effects. However, it will be appreciated from examination of Equation (1) that the factor μ may not simply be set to an arbitrarily high value much greater than 1. In such a case, the achieved control u becomes overly conservative with respect to the available control limits so that the resulting tracking performance may degrade significantly due to the underutilization of the available control.

The tradeoffs with respect to various values of the factor μ may be demonstrated by the following simulation example. Suppose an unstable open loop system has the following system dynamics:

${\overset{.}{x} = {{ax} + {{bu}_{\max}\mspace{14mu}{{sat}\left( \frac{u_{c}}{u_{\max}} \right)}}}},{{{where}\text{:}\mspace{14mu} a} = 0.5},{b = 2},{u_{\max} = 0.47}$ If $\delta = \left. {0.2\; u_{\max}}\rightarrow\begin{matrix} {u_{\max}^{\delta} = {{u_{\max} - \delta} = {0.8\; u_{\max}}}} \\ {{\overset{.}{x}}_{m} = {{- 6}\left( {x_{m} - {r(t)}} \right)}} \\ {{r(t)} = {0.7\left( {{\sin\left( {2\; t} \right)} + {\sin\left( {0.4\; t} \right)}} \right)}} \end{matrix} \right.$ The resulting simulation data may be seen in FIGS. 5 a through 5 d. In FIG. 5 a, the μ factor is set to zero such that u_(c)′ may exceed u_(max). With μ equaling zero, it may be seen that modified commanded input u_(c)′ equals the conventional commanded input u_(c) discussed with respect to FIG. 1. In FIG. 5 b, μ equals 1. This is still less than the minimum amount set by the Lyapunov conditions discussed earlier. Thus, u_(c)′ may again exceed u_(max). In FIG. 5 c, μ equals 10, which exceeds the minimally-required amount for Lyapunov stability so that u_(c)′ does not exceed u_(max). Thus, the tracking performance is significantly improved. However, μ cannot simply be increased indefinitely. For example, as seen in FIG. 5 d for a value of μ equals 100, the tracking performance has degraded significantly in that the modified commanded input u_(c)′ has become too damped in its response to changes in external conditions.

Those of ordinary skill in the art will appreciate that many modifications may be made to the embodiments described herein. Accordingly, although the invention has been described with respect to particular embodiments, this description is only an example of the invention's application and should not be taken as a limitation. Consequently, the scope of the invention is set forth in the following claims. 

1. An adaptive control method for a system having actuators controlling control surfaces, the actuators having an input command saturation of u_(max), the system having a control loop providing a linear feedback/feedforward commanded input of u_(lin) wherein u_(lin) is adaptively formed responsive to an error signal representing a difference between an actual state for the system and a reference state for the system, comprising: defining a positive input command limit u_(max) ^(δ)equaling (u_(max)−δ), where 0<δ<u_(max); defining a negative input command limit equaling −u_(max) ^(δ); if the absolute value of u_(lin) is less than or equal to u_(max) ^(δ), commanding the actuators with u_(lin); if u_(lin) exceeds u_(max) ^(δ), commanding the actuators with a first command input that is a function of the sum of u_(lin) and a scaled version of u_(max) ^(δ); and if u_(lin) is less than −u_(max) ^(δ); commanding the actuators with a second command input that is a function of the difference of u_(lin) and a scaled version of u_(max) ^(δ).
 2. The adaptive control method of claim 1, wherein u_(lin) is a function of both an adaptive command input and a nominal command input.
 3. The adaptive control method of claim 1, wherein u_(lin) is only a function of an adaptive command input.
 4. The adaptive control method of claim 1, further comprising: providing a scale factor μ, wherein the first input command equals (1/(1+μ))*(u_(lin)+μ u_(max) ^(δ)) and wherein the second input command equals ((1/(1+μ))*(ulin−μ u_(max) ^(δ)).
 5. The adaptive control method of claim 4, wherein μ is greater than
 1. 6. The adaptive control method of claim 4, wherein μ is greater than
 5. 7. The adaptive control method of claim 4, wherein μ is greater than
 100. 8. The adaptive control method of claim 1, wherein the system is an aircraft.
 9. The adaptive control method of claim 1, wherein the system is a spacecraft. 